Performance Comparison on Automated Generation of Coding Rules: A Case Study on ISO 26000
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Satoru Uchida | Tetsuya Nakatoh | Emi Ishita | Toru Oga | T. Oga | Emi Ishita | Satoru Uchida | Tetsuya Nakatoh
[1] Satoru Uchida,et al. Automated Generation of Coding Rules: Text-Mining Approach to ISO 26000 , 2016, 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI).
[2] Douglas W. Oard,et al. Thematic Analysis of Words that Invoke Values in the Net Neutrality Debate , 2015 .
[3] Douglas W. Oard,et al. Automatic Dictionary Extraction and Content Analysis Associated with Human Values , 2015 .
[4] Michael Scharkow,et al. Thematic content analysis using supervised machine learning: An empirical evaluation using German online news , 2011, Quality & Quantity.
[5] Fernando De la Torre,et al. Optimal feature selection for support vector machines , 2010, Pattern Recognit..
[6] Justin Grimmer,et al. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts , 2013, Political Analysis.
[7] Chih-Jen Lin,et al. Feature Ranking Using Linear SVM , 2008, WCCI Causation and Prediction Challenge.
[8] Amit Singhal,et al. Pivoted document length normalization , 1996, SIGIR 1996.
[9] Heiner Stuckenschmidt,et al. Multidimensional topic analysis in political texts , 2014, Data Knowl. Eng..
[10] Sayan Mukherjee,et al. Feature Selection for SVMs , 2000, NIPS.
[11] Marko Grobelnik,et al. Feature Selection Using Support Vector Machines , 2002 .
[12] Kevin Crowston,et al. Semi-Automatic Content Analysis of Qualitative Data , 2014 .
[13] Yuen-Hsien Tseng,et al. Trends of Science Education Research: An Automatic Content Analysis , 2010 .